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Predicting the future - big data, machine learning, and clinical medicine
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The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, and improve diagnostic accuracy.
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“A structured taxonomy helps organise the expanding field of AI in clinical decision-making and clarifies how different systems support patient care across clinical contexts [30,31].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [31]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Diagnostic decision support focuses on identifying disease states from imaging, laboratory, pathology, and symptom-based data, often using deep learning to extract patterns from high-dimensional inputs [31,32].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [31]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Risk stratification supports the identification of high-risk patients and enables targeted interventions and resource allocation [31].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [31]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In our case study, the model identified elements of the patient’s history and radiology findings to render its prediction, which are critical data points that a clinician would also use.73”
From Scalable and accurate deep learning with electronic health records (2018) · cites this paper as [73]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Currently, several LLMs, including versions of the ChatGPT family, are employed by both EHR systems and insurance companies38–40, creating a conflict of interest when similar datasets are used by entities with opposing objectives21,41.”
From Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability (2025) · cites this paper as [41]
Experimental — accuracy not yet measured
In the citing paper
“For example, data from various healthcare settings, which include different forms of bias and noise, may cause a model trained in the data of one hospital to fail to generalise to another [55].”
From Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond (2022) · cites this paper as [55]
In the citing paper
“Deep learning (DL) has the potential to transform medical diagnostics.”
From Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis (2021) · cites this paper as [2]
In the citing paper
“Similar to other fields and industries, AI is widely recognized in healthcare as a ground-breaking technological innovation that leads to better treatment outcomes [2–5].”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [4]
In the citing paper
“Evidence also suggests that while deep learning can identify spurious correlations, it often fails to determine causation, which can be essential in a clinical context [187].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [187]
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Citations
Cites
Machine Learning: An Applied Econometric Approach
Mullainathan, S., & Spiess, J. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.87 · Cited by 1,989
The Unreasonable Effectiveness of Data
Halevy, A. Y., Norvig, P., & Pereira, F. M. B. (2009) · IEEE Intelligent Systems · doi:10.1109/mis.2009.36 · Cited by 1,776
Restoring cortical control of functional movement in a human with quadriplegia
Bouton, C. E., Shaikhouni, A., Annetta, N. V., Bockbrader, M. A., Friedenberg, D. A., Nielson, D. M., Sharma, G., Sederberg, P. B., Glenn, B. C., Mysiw, W. J., et al. (2016) · Nature · doi:10.1038/nature17435 · Cited by 924
Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015) · American Economic Review · doi:10.1257/aer.p20151023 · Cited by 584
Self-citationSingle Reading with Computer-Aided Detection for Screening Mammography
Gilbert, F. J., Astley, S., Gillan, M. G., Agbaje, O. F., Wallis, M., James, J. J., Boggis, C. R. M., & Duffy, S. W. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0803545 · Cited by 296
Research works printed but not linked: 1
- Mullainathan S, J Econ Perspect (in press)
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Federico, C. A., & Trotsyuk, A. A. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-102623-104553 · Cited by 38
Clinical Application of Machine Learning Models for Early-Stage Chronic Kidney Disease Detection
Iftikhar, H., Hashem, A. F., Qureshi, M., & Rodrigues, P. C. (2025) · Diagnostics · doi:10.3390/diagnostics15202610 · Cited by 38
Machine Learning Will Change Medicine
Forsting, M. (2017) · Journal of Nuclear Medicine · doi:10.2967/jnumed.117.190397 · Cited by 38
Machine learning to identify multigland disease in primary hyperparathyroidism
Imbus, J. R., Randle, R. W., Pitt, S. C., Sippel, R. S., & Schneider, D. F. (2017) · Journal of Surgical Research · doi:10.1016/j.jss.2017.05.117 · Cited by 38
Big data in nephrology: Are we ready for the change?
Yang, C., Kong, G., Wang, L., Zhang, L., & Zhao, M. (2019) · Nephrology · doi:10.1111/nep.13636 · Cited by 38
The Advantages and Challenges of Using Real‐World Data for Patient Care
Ho, Y., Hu, F., & Lee, P. (2019) · Clinical and Translational Science · doi:10.1111/cts.12683 · Cited by 38
Functional Neuroimaging in the New Era of Big Data
Li, X., Guo, N., & Li, Q. (2019) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2018.11.005 · Cited by 38
Prediction of vestibular schwannoma recurrence using artificial neural network
Abouzari, M., Goshtasbi, K., Sarna, B., Khosravi, P., Reutershan, T., Mostaghni, N., Lin, H. W., & Djalilian, H. R. (2020) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.362 · Cited by 38
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DIAS, R. D., Shah, J., & Zenati, M. A. (2020) · Minerva Cardioangiologica · doi:10.23736/s0026-4725.20.05235-4 · Cited by 38
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Sammani, A., Baas, A. F., Asselbergs, F. W., & Riele, A. S. J. M. T. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10050921 · Cited by 38
Schenck, E. J., Hoffman, K. L., Cusick, M. M., Kabariti, J., Sholle, E., & Campion, T. R. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103789 · Cited by 38
Prediction of metabolic syndrome: A machine learning approach to help primary prevention
Tavares, L. D., Manoel, A., Donato, T. H. R., Cesena, F. Y., Minanni, C. A., Kashiwagi, N. M., da Silva, L. P., Amaro, E., & Szlejf, C. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.110047 · Cited by 38
Kressel, H. Y. (2016) · Radiology · doi:10.1148/radiol.2016162471 · Cited by 38
One-trial learning in larval DrosophilaPreprint
Weiglein, A., Gerstner, F., Mancini, N., Schleyer, M. H., & Gerber, B. (2019) · Learning & Memory · doi:10.1101/lm.049106.118 · Cited by 38
Early Analysis and Prediction of Fetal Abnormalities Using Machine Learning Classifiers
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Noh, E., Won, J., Jo, S., Hahm, D., & Lee, H. (2023) · Journal of Medical Internet Research · doi:10.2196/42238 · Cited by 37
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Townsend, B., Plant, K. L., Hodge, J., Ashaolu, O., & Călinescu, R. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1297073 · Cited by 37
Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review
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Ashraf, A. R., Somogyi‐Végh, A., Merczel, S., Gyimesi, N., & Fittler, A. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102844 · Cited by 37
Zhang, D., & Zhao, X. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108359 · Cited by 37
Application of machine learning based genome sequence analysis in pathogen identification
Gao, Y., & Liu, M. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1474078 · Cited by 37
Navigating ASEAN region Artificial Intelligence (AI) governance readiness in healthcare
Tun, H. M., Naing, L., Malik, O. A., & Rahman, H. A. (2025) · Health Policy and Technology · doi:10.1016/j.hlpt.2025.100981 · Cited by 37
Artificial Intelligence (AI) and Emergency Medicine: Balancing Opportunities and Challenges
Amiot, F., Potier, B., Viard, T., Tellier, É., & Peschanski, N. (2025) · JMIR Medical Informatics · doi:10.2196/70903 · Cited by 37
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As’ad, M., Faran, N., & Joharji, H. (2025) · JMIR AI · doi:10.2196/75866 · Cited by 37
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Steinhoff, G., Nesteruk, J., Wolfien, M., Große, J., Ruch, U., Vasudevan, P., & Müller, P. (2017) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2017.10.007 · Cited by 37
Lorenzo, A. J., Rickard, M., Braga, L. H., Guo, Y., & Oliveria, J. P. (2018) · Urology · doi:10.1016/j.urology.2018.05.041 · Cited by 37
Stuckey, T., Gammon, R. S., Goswami, R., Depta, J. P., Steuter, J., Meine, F. J., Roberts, M. C., Singh, N., Ramchandani, S., Burton, T., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198603 · Cited by 37
Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task
Siyar, S., Azarnoush, H., Rashidi, S., Winkler-Schwartz, A., Bissonnette, V., Ponnudurai, N., & Maestro, R. F. D. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02155-3 · Cited by 37
Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques
Ezzati, A., Harvey, D. J., Habeck, C. G., Golzar, A., Qureshi, I., Zammit, A. R., Hyun, J., Truelove‐Hill, M., Hall, C. B., Davatzikos, C. A., et al. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-191038 · Cited by 37
Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing
Saunders, G. H., Christensen, J. H., Gutenberg, J., Pontoppidan, N. H., Smith, A. D., Spanoudakis, G., & Bamiou, D. (2020) · Ear and Hearing · doi:10.1097/aud.0000000000000850 · Cited by 37
Mermod, M., Jourdan, E., Gupta, R., Bongiovanni, M., Tolstonog, G. V., Simon, C., Clark, J. R., & Monnier, Y. (2020) · Head & Neck · doi:10.1002/hed.26105 · Cited by 37
Yang, S. W., Hyon, Y. K., Na, H. S., Jin, L., Lee, J. G., Park, J. M., Lee, J. Y., Shin, J. H., Lim, J. S., Na, Y. G., et al. (2020) · BMC Urology · doi:10.1186/s12894-020-00662-x · Cited by 37
Prediction of Long-term Cognitive Function After Minor Stroke Using Functional Connectivity
Lopes, R., Bournonville, C., Kuchcinski, G., Dondaine, T., Mendyk, A., Viard, R., Pruvo, J., Hénon, H., Georgakis, M. K., Duering, M., et al. (2021) · Neurology · doi:10.1212/wnl.0000000000011452 · Cited by 37
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Zhang, W., & Wang, M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0255906 · Cited by 37
Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences
Wilson, B. S., Tucci, D. L., Moses, D. A., Chang, E. F., Young, N. M., Zeng, F., Lesica, N. A., Bur, A. M., Kavookjian, H. L., Mussatto, C. C., et al. (2022) · Journal of the Association for Research in Otolaryngology · doi:10.1007/s10162-022-00846-2 · Cited by 37
Wu, T., Duan, Y., Zhang, T., Tian, W., Liu, H., & Deng, Y. (2022) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2709254 · Cited by 37
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Tsolakis, I. A., Kolokitha, O.-E., Papadopoulou, E., Tsolakis, A. I., Kilipiris, E., & Palomo, J. M. (2022) · Life · doi:10.3390/life12111894 · Cited by 37
Lakshmanan, K., Tessicini, F., Gil, A. J., & Auricchio, F. (2023) · Applied Mathematical Modelling · doi:10.1016/j.apm.2023.07.001 · Cited by 37
A bibliometric analysis of the advance of artificial intelligence in medicine
Lin, M., Lin, L., Lin, L., Lin, Z., & Yan, X. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1504428 · Cited by 36
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Dellino, M., Cerbone, M., d’Amati, A., Bochicchio, M. A., Laganà, A. S., Etrusco, A., Malvası, A., Vitagliano, A., Pinto, V., Cicinelli, E., et al. (2024) · AI · doi:10.3390/ai5040144 · Cited by 37
Therapeutic Systems and Technologies: State-of-the-Art Applications, Opportunities, and Challenges
Almekkawy, M. K., Zderic, V., Chen, J., Ellis, M. D., Haemmerich, D., Holmes, D. R., Linte, C. A., Panescu, D., Pearce, J. A., & Prakash, P. (2019) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2019.2908940 · Cited by 36
Clinical decision support model for tooth extraction therapy derived from electronic dental records
Cui, Q., Chen, Q., Liu, P., Liu, D., & Wen, Z. (2020) · Journal of Prosthetic Dentistry · doi:10.1016/j.prosdent.2020.04.010 · Cited by 36
Huang, Z., Martin, J. T., Huang, Q., Ma, J., Pei, F., & Huang, C. Y. (2021) · International Journal of Surgery · doi:10.1016/j.ijsu.2021.106183 · Cited by 36
Jalalinajafabadi, F., Stadler, M. A., Dand, N., Jadon, D. R., Soomro, M., Ho, P., Marzo‐Ortega, H., Helliwell, P., Korendowych, E., Simpson, M. A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-00854-x · Cited by 36
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Rani, S. V. J., Chandran, K. R. S., Ranganathan, A., Chandrasekharan, M., Janani, B., & Deepsheka, G. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-03823-y · Cited by 36
Wu, T., Wei, Y., Wu, J.-B., Yi, B., & Li, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-28421-6 · Cited by 36
Wu, X., Sun, Y., Xu, X. L., Steyerberg, E. W., Helmrich, I. R., Lecky, F. E., Guo, J., Li, X., Feng, J., Mao, Q., et al. (2023) · Journal of Neurotrauma · doi:10.1089/neu.2022.0221 · Cited by 36
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Rambachan, A. (2024) · The Quarterly Journal of Economics · doi:10.1093/qje/qjae013 · Cited by 36
Fardous, M., & Hasan, M. (2021) · doi:10.63125/3f0mwc90 · Cited by 36
Ye, J., Hai, J., Song, J., & Wang, Z. (2024) · medRxiv · doi:10.1101/2024.05.01.24306690 · Cited by 36
Kom, I. Y., Dongelmans, D. A., de Keizer, N. F., Jager, K. J., Schut, M. C., Abu–Hanna, A., & Klopotowska, J. E. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad014 · Cited by 35
Machine learning‐based prediction models for pressure injury: A systematic review and meta‐analysis
Pei, J., Guo, X., Tao, H., Wei, Y., Zhang, H., Ma, Y., & Han, L. (2023) · International Wound Journal · doi:10.1111/iwj.14280 · Cited by 35
Weidener, L., & Fischer, M. (2024) · JMIR AI · doi:10.2196/51204 · Cited by 35
Daniel, R., Jones, H., Gregory, J. W., Shetty, A., Francis, N., Paranjothy, S., & Townson, J. K. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00050-5 · Cited by 35
Garcia‐Vozmediano, A., Maurella, C., Ceballos, L. A., Crescio, E., Meo, R., Martelli, W., Pitti, M., Lombardi, D., Meloni, D., Pasqualini, C., et al. (2024) · Veterinary Research · doi:10.1186/s13567-024-01323-9 · Cited by 35
Dhanda, S. S., Panwar, D., Lin, C., Sharma, T. K., Rastogi, D., Bindewari, S., Singh, A. M., Li, Y., Agarwal, N. K., & Agarwal, S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01201-x · Cited by 35
Muhlestein, W. E., Akagi, D. S., Kallos, J. A., Morone, P. J., Weaver, K. D., Thompson, R. C., & Chambless, L. (2017) · Journal of Neurological Surgery Part B Skull Base · doi:10.1055/s-0037-1604393 · Cited by 35
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Kraus, J. M., Lausser, L. M., Kühn, P., Jobst, F., Bock, M., Halanke, C., Hummel, M., Heuschmann, P. U., & Kestler, H. A. (2018) · International Journal of Data Science and Analytics · doi:10.1007/s41060-018-0095-0 · Cited by 35
Song, Y., Gao, S., Tan, W., Qiu, Z., Zhou, H., & Zhao, Y. (2018) · Journal of Cancer · doi:10.7150/jca.26649 · Cited by 35
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Kearney, E., Wojcik, A., & Babu, D. (2019) · Journal of Genetic Counseling · doi:10.1002/jgc4.1192 · Cited by 35
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Pelaccia, T., Forestier, G., & Wemmert, C. (2019) · Canadian Medical Association Journal · doi:10.1503/cmaj.190506 · Cited by 35
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Mehta, N. B., Lee, C. S., Mendonça, L. S. M., Raza, K., Braun, P. X., Duker, J. S., Waheed, N. K., & Lee, A. (2020) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2020.2769 · Cited by 35
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Sorell, T., Rajpoot, N., & Verrill, C. L. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-107024 · Cited by 35
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Ndikum, P. (2020) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2004.01504 · Cited by 35
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dos Santos, H. G., do Nascimento, C. F., Izbicki, R., Duarte, Y. A. O., & Filho, A. D. P. C. (2019) · Cadernos de Saúde Pública · doi:10.1590/0102-311x00050818 · Cited by 34
Lin, C.-H., Hsu, K.-C., Johnson, K. R., Luby, M. L., & Fann, Y. C. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.103988 · Cited by 34
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Wenderott, K., Krups, J., Luetkens, J. A., & Weigl, M. (2024) · Applied Ergonomics · doi:10.1016/j.apergo.2024.104243 · Cited by 33
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Metadata
- Authors
- Obermeyer, Z., & Emanuel, E. J.
- Affiliations
- Brigham and Women's Hospital (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2016-09-29
- Volume
- 375
- Issue
- 13
- Pages
- 1216-1219
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC5070532
- OpenAlex
- W2525984666
- Semantic Scholar
- 4d1fdd81f033cd58f3723bfc61e7d12079647a7a
- Cited by
- 3,652
- References
- 5
- Citations by year
- 2026: 494 · 2025: 596 · 2024: 492 · 2023: 378 · 2022: 371 · 2021: 389 · 2020: 348 · 2019: 286 · 2018: 195 · 2017: 95 · 2016: 3
- FWCI
- 140.21
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP5OD012161)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- machine learning, big data, clinical medicine, outcome prediction, diagnostic accuracy, radiology, pathological anatomy
- MeSH terms
- Machine Learning, Algorithms, Diagnosis, Expert Systems, Prognosis, Clinical Medicine, Computational Biology
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